Machines such as, for example, dozers, motor graders, wheel loaders, wheel tractor scrapers, and other types of heavy equipment are used to perform a variety of tasks. Remotely controlled, autonomously controlled, and semi-autonomously controlled machines are capable of operating with little or no human input by relying on information received from various machine systems. For example, based on machine movement input, terrain input, and/or machine operational input, a machine can be controlled to remotely and/or automatically complete a programmed task. By receiving appropriate feedback from each of the different machine systems during performance of the task, continuous adjustments to machine operation can be made that help to ensure precision and safety in completion of the task. In order to do so, however, the information provided by the different machine systems should be accurate and reliable. The velocity of the machine is one such parameter whose accuracy may be important for control and positioning of the machine.
To calculate the velocity, conventional machines typically utilize a positioning system, which can be used to determine various operating parameters of the machine such as velocity, pitch rate, yaw rate, roll rate, etc. Conventionally, the positioning system utilizes Global Positioning System (GPS) data along with data from an Inertial Measurement Unit (IMU), which typically includes one or more accelerometers, to calculate velocity. Low cost MEMS accelerometers have error sources such as bias drift that quickly change and, if velocity was calculated solely based on the readings from the accelerometer, the velocity reading will become inaccurate over time. Therefore, conventional positioning systems use a Kalman filter to account for the accelerometer errors by using the GPS velocity measurement, which is fairly accurate.
An exemplary system that may be used to control a machine is disclosed in PCT Publication No. WO 97/24577 to Croyle et al. that published on Jul. 10, 1997 (“the '577 PCT Publication”). The system of the '577 PCT Publication utilizes information from a Global Positioning System (GPS) to obtain velocity vectors, which include speed and heading components, for “dead reckoning” the vehicle position from a previous position. If information from the GPS is not available, then the vehicle navigation system utilizes information from an orthogonal axes accelerometer, such as two or three orthogonally positioned accelerometers, to propagate vehicle position.
Although the system of the '577 PCT Publication may be useful in determining the velocity, the system may not provide accurate velocity when the GPS signal is lost or unavailable for an extended period of time. This is because the readings from the accelerometers drift over time such that the errors accumulate and provide an incorrect velocity reading.
The velocity estimation system of the present disclosure is directed toward solving one or more of the problems set forth above and/or other problems of the prior art.